---
title: "Flex Dashboards for Sharing Results"
output:
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: columns
    theme: cosmo
editor_options: 
  chunk_output_type: console
---

```{r, libraries, include = FALSE}
## here to navigate project directory
library(here)

## tidyverse for data wrangling and visualization
library(tidyverse)

## snakecase for naming conventions
library(snakecase)

## flexdashboard to build dashboards in R Markdown
library(flexdashboard)

## shiny for reactive dashboards
library(shiny)

## jsonlite to import json data files
library(jsonlite)

## hexbin for hex plots
library(hexbin)

## DT for web-friendly, interactive tables
library(DT)
```

```{r, global, include = FALSE}
### import data objects
## use read_json() to import the data file
airline <- read_json(
  ## use here() to locate file in our project directory
  here(
    # folder
    "data", 
    # file
    "airline.json"
  ),
  ## turn into data table
  simplifyVector = TRUE
)

### rename variables, convert variables
## overwrite 
airline <- airline %>%
  ## convert to tibble
  as_tibble() %>%
  ## mutate variable types and values
  mutate(
    ## characters to factors
    across(
      # find character variables
      .cols = where(is.character),
      # convert to factors
      .fns = as_factor
    ),
    ## relevel
    Class = fct_relevel(
      # factor
      Class, 
      # level
      "Business", 
      # position
      after = 2
    )
  ) %>%
  ## clean variable names
  rename_with(
    # function
    .fn = to_snake_case
  )
```

```{css, echo = FALSE}
/* adjust height of data tables */
.dataTables_scrollBody {
  /* adjust height */
  max-height: 100% !important;
}
```

Plots 
================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Violin Plot

```{r, plot_1}
### violin plot
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = customer_type, 
    # y-axis
    y = recommendation, 
    # fill
    fill = class
  )
) +
  ## violin geometry
  geom_violin() +
  ## labels
  labs(
    # x-axis
    x = "Customer Type", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Class"
  )
```

### Hex Bin Plot 

```{r, plot_2}
### hex plot
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = age, 
    # y-axis
    y = recommendation
  )
) +
  ## hex geometry
  geom_hex() +
  ## labels
  labs(
    # x-axis
    x = "Age", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Count"
  )
```

### Heat Map

```{r, plot_3}
### heat map
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = leg_room, 
    # y-axis
    y = cleanliness, 
    # fill
    fill = recommendation
  )
) +
  ## tile geometry
  geom_tile() +
  ## adjust fill color
  scale_fill_gradient2(
    # low value color
    low = "blue",
    # mid value color
    mid = "white",
    # high value color
    high = "red",
    # midpoint
    midpoint = 50
  ) +
  ## labels
  labs(
    # x-axis
    x = "Leg Room", 
    # y-axis
    y = "Cleanliness", 
    # fill
    fill = "Recommendation"
  )
```

### 2D Density Plot

```{r, plot_4}
### filled 2d density
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = flight_distance, 
    # y-axis
    y = recommendation
  )
) +
  ## tile geometry
  geom_density_2d_filled(
    # contours
    contour_var = "ndensity"
  ) +
  ## facet
  facet_grid(
    # rows by columns
    customer_type ~ class
  ) +
  ## labels
  labs(
    # x-axis
    x = "Flight Distance", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Density"
  )
```

Table 1 {data-navmenu=Tables} 
================

Column
-----------------------------------------------------------------------

### Younger Female Customers

```{r, table_1}
# Create the filtered data table
tab_1 <- airline %>%
  filter(age <= 30, gender == "Female")

# Create the interactive table
datatable(tab_1, 
          rownames = FALSE, 
          extensions = 'Scroller', 
          options = list(
            dom = 'tlpS',
            scrollY = '200px',
            pageLength = 25
          ))
```

Table 2 {data-navmenu=Tables} 
================

Column
-----------------------------------------------------------------------

### Older Female Customers

```{r, table_2}
# Create the filtered data table
tab_2 <- airline %>%
  filter(age > 30, gender == "Female")

# Create the interactive table
datatable(tab_2, 
          rownames = FALSE, 
          extensions = 'Scroller', 
          options = list(
            dom = 'tlpS',
            scrollY = '200px',
            pageLength = 25
          ))
```